Understanding and Improving Human Data Relations

Alex Bowyer

7 Discussion II: Designing and Pursuing Better Human Data Relations

“Civilizations advance not by the technology they know about, but by the technology they don’t have to know about.” – Anonymous proverb

7.1 Introduction & Background

Through the Case Studies (Chapter 4 & 5) and the discussion in Chapter 6, a clear understanding of what people want from direct and indirect data relations (RQ1 & RQ2) has been established. In this chapter, we turn our attention from theory to practice, from what is needed to what is possible. Specifically, this chapter will return to the overall research question and consider “How might [better Human Data Relations] be achieved?”, and answer this question by describing practical approaches for future research, innovation and policy that are either novel or already emergent.

This chapter is deliberately broad and open ended. It does not pretend to be complete or definitive in its interpretation of the outlook for HDR. It is not a roadmap, but rather a snapshot of ongoing work, identified challenges and known opportunities, forming an anthology of reference material, based on my research and design experience from my six years working to understand and advance HDR.

The rationale here is that it will be valuable for anyone working in the HDR space to have a good high-level understanding of the landscape as well as specific ideas to work with; the goal is to boost and strengthen any such activities so that they might benefit from the insights gained.

The shape this chapter takes is to consider the the six HDR wants that this thesis has uncovered [Chapter 6] as a basis for defining objectives for the HDR landscape, then to to illustrate what specific obstacles and opportunities are relevant when attempting to pursue those objectives, as well as to highlight specific designerly insights that are relevant.

There are many aspects to the wide-reaching objective of better HDR in practice: technical, design, commercial, legal, moral, social and political and this chapter does not cover them all, nor is it formal empirical research. Instead, detail is provided in the form of real world practical designs and insights from four industrial and academic research projects I was part of during the same timeframe as the empirical research, as well as from the work of other innovators and activists. This detail is contextualised relative to existing literature and the thesis’ earlier contributions.

Some of the challenges and opportunities herein are described in greater detail than others, corresponding only to my proximity and depth of engagement with those ideas rather than their relative merit, complexity or impact potential. I consider that it is more useful to introduce a range of applicable ideas even if some are only lightly detailed, rather than to detail just a few.

In section 7.1.1 the peripheral R & D activities I undertook are described; forming the primary point of reference for this chapter, as this peripheral work has informed and allowed me to build upon the core HDR understanding from the empirial research, and much of the work has often aligned well to the six data wants [Chapter 6]. Often the work has exposed evolving areas where different actors are trying to bring about better HDR.

In section 7.1.2, I explain some important context about the nature of the ideas presented in this chapter and how to attribute them fairly.

In section 7.1.3, I introduce some additional background on Theories of Change (ToC), which are used as a framing device for structuring the opportunities described in the main body of this chapter into a series of different possible trajectories for change.

In section 7.1.4, I consider the researcher-turned-activist stance that drives this chapter, framing the pursuit of better HDR as a recursive public.

In section 7.2, I formalise and expand the Human Data Relations concept. Additional insights into how people relate to data are identified, as well an important dichotomy of two distinct drivers that motivate people’s needs for better relations with their data.

Section 7.3 and 7.4 form the main body of this chapter, with obstacles and insights being detailed in section 7.3 and specific opportunities into how better Human Data Relations can be pursued in practice being described in 7.4. 7.4 identifies four specific trajectories of change (interpreted using the ToC frame described in 7.1.3). Within each of these four trajectories, specific named opportunities are described.

Section 7.5 concludes the thesis, summarising the change trajectories presented in 7.4, reflecting on my journey as a researcher and summarising the thesis’ contributions as a whole.

7.1.1 Peripheral Research & Design Settings

[TODO Move 3.4.3 etc. to here and remove all refs to 3.4.3]

The majority of examples and learnings shared in this chapter come from my participation as an expert researcher and designer in two industrial research projects:

  1. BBC R&D’s Cornmarket Project, which explored through user experience design, technical prototyping and participatory research, how individuals might interact with data through a Personal Data Store interface (see 3.4.3.3)
  2. Sitra/Hestia.ai’s #digipower Project, a successor to Case Study Two, in which European politicians examined companies’ data practices through exercising data rights and conducting technical audits (see 3.4.3.4)

In addition, my participation as an interface designer and front-end software developer in the following two academic research projects contributes secondarily to this chapter:

  1. Connected Health Cities (CHC)’s SILVER Project, where I, along with a backend developer and a team of researchers, developed a prototype health data viewing interface for Early Help support workers (see 3.4.3.1).
  2. Digital Economy Research Centre (DERC)’s Healthy Eating Web Augmentation Project, which explored the use of web augmentation techniques to modify the user interface of takeaway service Just Eat to insert health information, in support of healthy eating (see 3.4.3.2).

7.1.2 Attribution of Insights

While this thesis is my own original work, and many ideas presented in this chapter are fully original, some of the specific details, theories and ideas presented in this chapter arose or were developed or augmented through my close collaboration, discussion and ideation with other researchers, including:

  • Jasmine Cox, Suzanne Clarke, Tim Broom, Rhianne Jones, Alex Ballantyne and others at BBC R&D;
  • Paul-Olivier Dehaye, Jessica Pidoux, Francois at Hestia.ai;
  • Stuart Wheater of Arjuna Technologies and Kyle Montague of Open Lab during the SILVER project; and
  • Louis Goffe of Open Lab on the DERC Healthy Eating project
  • earlier innovation work with Alistair Croll at Rednod, Montréal, Canada (circa 2011) and with Megan Beynon at IBM Hursley, UK (circa 2006).

Due to these collaborations and the ongoing and parallel nature of many of these projects to my PhD research, it is impossible to precisely delineate the origin of each idea or insight. In practice, ideas from my developing thesis and own thinking informed the projects’ trajectories and thinking, and vice-versa. These ideas would not have emerged in this form without my participation, so they are not the sole intellectual property of others, but equally I would not have reached the same conclusions alone, so the ideas are not solely my own either. All diagrams and illustrations were produced by me, except where specified, and the overall synthesis and framing presented in this chapter is my own original work. Where this chapter includes material from the four projects, that material is either already public, or permission has been obtained from the corresponding project teams.

7.1.3 Theories of Change

To provide a structure for cataloguing the insights conveyed by this chapter, I use a Theory of Change (ToC) framing. ToC is a set of methodologies is commonly used by philanthropists, educators and those trying to improve the lives of disadvantaged populations (Brest, 2010); the theories can be used in different ways including planning, participatory design and field evaluation of the effectiveness of new initiatives. There are many different implementations, but common to most of them is a focus on explicitly mapping out desired outcomes (Taplin and Clark, 2012) with a clear focus on who is acting and whether the change being brought about is a change in action, or a change in thinking (Es, Guijt and Vogel, 2015). In this chapter, ToC theory will be used in a very limited way, not as a methodology but simply to provide a structural frame for proposed changes, as described below. Using ToC to perform evaluation of the effectiveness of proposed change approaches in action in society would be well beyond the scope of this thesis. Nonetheless, this frame is a useful way to map out the different approaches to changing the world in pursuit of the ideal of better HDR.

Figure 29: The Four Dimensions of Change

Figure 29 illustrates the aspects of ToC thinking that section 7.4 will use as its frame. Specifically, desired changes can be broken down into:

  • Internal changes: changes in thinking, feeling, reasoning, understanding, attitudes or identity.
  • External changes: changes in actions, behaviour, interactions, structure, policy, technological capability, processes and the external environment.

At the same time, desired changes can be broken down into:

  • Individual changes: changes to individual thought or actions
  • Collective changes: changes to the thoughts or actions of groups of people together, or to the systems, practices and norms of society at large.

These two splits produce four dimensions of change, and form four quadrants representing different types of change, which are shown in Figure 29 and described here:

  • Individual/Internal (II): This top-left quadrant represents changes to what individuals know and understand, and to how they think, feel and plan to take action.
  • Individual/External (IE): This top-right quadrant represents changes to how individuals’ relationships with others; acting (or being enabled to act) differently in their daily lives and when interacting within society.
  • Collective/Internal (CI): This bottom-left quadrant represents changes in the shared knowledge of groups of people or to the collective identity or values of social groups.
  • Collective/External (CE): This bottom-right quadrant represents changes to the structures and procedures within which people operate, including technology, law, societal norms and communications.

Key to ToC thinking is the idea that making changes in one quadrant can stimulate change in others; for example, collective learning about data attitudes and practices, such as the research conducted in this PhD, (lower left quadrant) could inform the design of new technologies, interfaces or processes (lower right quadrant), which if built could make new structures available to have an impact on improving individual-provider relationships (upper-right quadrant). The changes to those relationships could then in turn lead to individuals thinking and feeling differently (upper left quadrant), for example feeling more empowered or having greater awareness of data practices.

7.1.4 Better Human Data Relations as a Recursive Public

Before engaging with the practicalities of pursuing change, it is valuable to revisit the stance from which we approach this change. As outlined in 3.2, the research of this PhD has been grounded in participatory action research and experience-centred design; by using a Digital Civics (Vlachokyriakos et al., 2016) frame to gain deep understanding of people’s needs and the ways those needs are not fully met, we can see how the world needs to change. Section 3.2 already outlined that we can consider such research as political, seeking to correct an imbalance in the world. In this chapter, we look beyond identifying what change is needed, and step into the role of activist, exploring how individuals and groups can actually change the world they inhabit.

In doing so, we can consider ourselves (those who pursue better Human Data Relations, or HDR reformers as a shorthand) as a recursive public (Kelty, 2008; ‘Recursive Public (Discussion Page)’, no date), albeit a nascent one. This is a term originating in the free software movement to describe a “collective, independent of other forms of constituted power, capable of speaking to existing forms of power through the production of actually existing alternatives”. This term captures the idea that through various means at our disposal: participatory research, experience-centred design, engineering software prototypes, exertion of legal rights, and efforts to raise public awareness, we seek to modify the systems and practices we live within in pursuit of our goals. This collective around better Human Data Relations does not yet exist as a named and identifiable public (Le Dantec, 2016) but its members congregate around emergent collectives in interconnected and overlapping spaces, most notably the MyData community (MyData, 2017) and its members, but also research and activism agendas including but not limited to: digital rights (‘Open rights group: Who we are’, no date), gig economy worker rights (Kirven, 2018), privacy by design (Cavoukian, 2010), data justice (Taylor, 2017; Crivellaro et al., 2019), critical algorithm studies (Gillespie and Seaver, 2016), humane technology (Harris, 2013) and explainable AI (‘Explainable AI: Making machines understandable for humans’, no date).

Whether these disparate groups coalesce into a single identifiable public remains to be seen, and so too whether the term this thesis offers of Human Data Relations is sufficient to capture that public (at least, it provides a descriptive umbrella term). Nonetheless, the breadth of research and innovation and activism happening in this space validates both the need and the desire for such a recursive public around better HDR to exist. Therefore, this chapter takes an unashamedly critical view of the status quo, favouring disruptive societal changes that would further the objectives of better Human Data Relations and providing actionable approaches that will be of use to the members of this public. The chapter asks, “How can we change the world into the one we want?”

7.2 Refining and Defining ‘Human Data Relations’ (HDR)

[TODO ADD A TWO SENTENCE DEFINITION, MORE CLEARLY FRAME THE NEW FIELD] [ reference HDI style, Human “data” relations, human/data relations (like PR ref C5) ]

Chapter 6 established six ‘wants’ that people have in their relationships with data: visible, understandable and usable data; process transparency, individual oversight and decision-making involvement. At a simplistic level therefore ‘better’ HDR can be achieved by working to improve upon those six aspects of data interaction. However, as this section will explain, HDR can be conceptually split into two distinct motives, to which those six wants apply differently, therefore it is useful to develop the concept of HDR further. As background understanding for this duality of motivation, it is first necessary to examine more closely what role data plays in people’s lives.

7.2.1 The Role of Personal Data

In the modern world, where almost anything can be encoded as data, and given many previously analogue objects and activities now have digital equivalents, the concept of data has become broad and hard to pin down. Underlying Human Data Relations is to explain what roles data can play in people’s lives – what it is to people. Through the Case Studies, external work and my prior learning, I have so far identified 8 distinct lenses to consider how people might relate to it. These are modelled in Table 15.

Table 15. Eight lenses on data.
Way of thinking about data Explanation & Implications
Data as property Data can be considered as a possession. This highlights issues of ownership, responsibility, liability and theft.
Data as a source of information about you Knowing that data contains encoded assertions about you and can be used to derive further conjectures enables thinking about how it might be exploited by others, but also how you can explore and use it yourself for reflection, asking questions, self-improvement and planning. It invites consideration of the right to access, data protection, and issues around accuracy, fairness and misinterpretation / misuse.
Data as part of oneself A photo or recording of you, or a typed note or search that popped into your head could be deeply personal. This lens on data highlights issues around emotional attachment/impact, privacy, and ethics.
Data as memory Data can be considered as an augmentation to one’s memory, a digital record of your life. This lens facilitates design thinking around search and recall, browsing, summarising, cognitive offloading, significance/relevance, and the personal value of data.
Data as creative work Some of the data we produce (e.g. writing, videos, images) can be considered as an artistic creation. This lens enables thinking about attribution, derivation, copying, legacy and cultural value to others.
Data as new information about the world Data created by others can inform us about previously unknown occurrences in our immediate digital life or the wider world. This lens is useful for thinking about discovery, recommendations, bias, censorship, filter bubbles, and who controls the information sources we use, as well as who will see and interpret data that we generate and what effects our data has on others.
Data as currency Many data-centric services require data to be sacrificed in exchange for access to functionality, and some businesses now explicitly enable you to sell your own data. This lens highlights that data can be thought of as a tradable asset, and invites consideration of issues of data’s worth, individual privacy, exploitation and loss of control.
Data as a medium for thinking, communicating and expression Some people collect and organise data into curated collections, or use it to convey facts and ideas, to persuade or to evoke an emotional impact. This lens is useful to consider data uses such as lists, annotation, curation, editing, remixing, visualisation and producing different views of data for different audiences.

When considering HDR, it is important to recognise that people may think of their personal data through any or all of these ‘lenses’ [Karger et al. (2005);2.2.2] at any given time, and any process or system design involving data interaction should take these into account.

Looking across this set of lenses, it is possible to identify four specific roles that data can serve:

  1. Data has a role as an artifact of value to your life;
  2. Data has a role in informing you about yourself, the world, and the prior or recent actions of others that may affect you;
  3. Data has a role as a usable material with which to effect change in your life;
  4. Data has a role as a means to monitor changes in data holders’ behaviours, digital influences upon you or changes within your life.

7.2.2 Human Data Interaction or Human Information Interaction?

To unpack HDR further, it is important to highlight the difference between humans relating to data, and humans relating to information. Human Data Interaction (HDI) concerns the way people interact with data. Mortier et al. (Mortier et al., 2013, 2014) defined the field of HDI without distinguishing data (the digital artifact stored on computer) from information (the facts or assertions that said data can provide when interpreted). This is an important distinction. The parallel field of Human Information Interaction (HII) originated in library sciences, and considers the way humans relate to information without regard to the technologies involved (Marchionini, 2008). William Jones et al. called for a new sub-field of HII in an HCI context2, observing that it is important to include a focus on information interaction because HCI can “unduly focus attention on the computer when, for most people, the computer is a means to an end – the effective use of information” (Jones et al., 2006). DIKW theory [2.1] highlights that interpretation of data to obtain information is a discrete activity. This was borne out in the findings of Case Study Two, where it became clear that participants have distinct needs from data, and from information (5.4.3.2). Access to data and information is critical to both understanding and useability, as detailed in section 6.1.2 and 6.1.3.

Drawing on this theory, we can see then that in considering Human Data Relations, there are in fact three distinct artifacts to consider:

  1. data - the stored digital artifacts pertaining to users held by organisations for algorithmic processing and human reference, copies of which can be obtained using individual data rights.
  2. information about individuals - the collection of facts and assertions about the individual and their life, which are obtained through human or algorithmic interpretation of stored data (or in some organisations’ case, through analytical inference).
  3. information about data (also categorised in Table 9 / 5.3.1 as metadata) - stored facts about the data, such as where it has been stored, who has accessed it, how it was collected, what it means, or when it has been shared externally.

7.2.3 The Two Distinct Motivations for Human Data Relations

By making this distinction between the two types of information which people might interact with, and considering the six wants in Chapter 6, it becomes clear that there are two very different reasons why people might want better HDR:

  1. to acquire information about one’s data, so that one might exert control over and make informed choices about where the data is held and how it is used, in order to be treated fairly and gain more control over the use of one’s personal data. This is Personal Data Ecosystem Control (PDEC).

  2. to acquire information about oneself, so that one might gain insights into one’s own behaviour and gain personal benefits from those insights or them to make changes in one’s life. This is Life Information Utilisation (LIU).

The two distinct processes that individuals might go through in pursuit of these motives are exemplified in Figure 30. PDEC is a process of holding organisations to account over and managing what happens to personal data, often regardless of what it means, whereas LIU is more concerned with what the data means and its inherent value as encoded life information, regardless of where it is stored and how it is used3. This novel way of modelling the motivations for data interaction were first proposed in my 2021 workshop paper (Bowyer, 2021).

Figure 30: The Two Motivations for HDR: Controlling your personal data ecosystem and utilising your information about your life, with ‘idealised’ processes illustrated

7.2.3.1 Life Information Utilisation

Life Information Utilisation is a superset of Self Informatics (SI) 2.2.3. It includes all purposes relating to self-monitoring and self-improvement through data, but also includes all other uses of personal data including creative expression, evidence gathering, nostalgia, keeping, and sharing. Many of these desires were expressed in Case Study Two (see Table 12 in 5.3.3), and also hinted at in the Early Help context [4.4.1]. While the existence of digitally-encoded information clearly unlocks new possibilities, LIU has existed in some form throughout human civilisation, as seen through analogue processes such as storytelling, journalling, scrapbooking, arts and crafts.

In the LIU context, the most important wants to focus on improving are data understandability (6.1.2) and data useability15 (6.1.3), which relate closely to the HDI concepts of legibility and agency respectively.

7.2.3.2 Personal Data Ecosystem Control

Unlike LIU, Personal Data Ecosystem Control is an individual need that is new; arising as a result of the emergence of the data-centric world (2.1, 2.2.4). Only when organisations began to collect and store facts about people as a substitute for direct communication and involvement did it become necessary. The more data is collected about individuals, and the more parties collect and share that data, the greater the need for individuals to learn about that data so that they might influence its use (or risk their lives being affected in unexpected or potentially unfair ways). PDEC is a direct response to the power imbalance between data holders and individuals that the World Economic Forum described in 2014 [Hoffman (2014);2.1.2].

In the PDEC context, multiple data wants are important: visible data and transparent processes, as well as individual oversight and involvement. For simplicity, the former two wants can be referred to collectively as “ecosystem transparency”, and the latter two as “ecosystem negotiability” (drawing on the HDI concept of negotiability), and these terms will be used below.

7.3 The landscape of opportunity: Obstacles to better Human Data Relations, and how we might overcome them

Figure X: Mapping the Six Wants into Objectives for the HDR Opportunity Landscape

In order to provide value to future researchers, activists and innovators, this chapter contributes a map of the HDR opportunity landscape. This map is expressed in two parts across this section and 7.4. As a first step, we can take the “six wants from data relations” Chapter 6, and map reduce those to four simple ‘landscape objectives’ which shape our ultimate goals for effective HDR in this landscape of opportunity:

  1. Data Awareness & Understanding;
  2. Data Useability15;
  3. Ecosystem Awareness & Understanding and
  4. Ecosystem Negotiability.

As Figure X shows, the need for data to be understandable, visible and useable, applies to all data, whether that data is interpretable as life information or ecosystem information.

Using these four objectives as our goals, and considering how they might be tackled, specific obstacles have been identified. These are analogous to Li’s ‘barriers cascade’ [2.2.3;#li2010] and represent the obstacles that individuals or system designers must be empowered to overcome if the objectives are to be met. These obstacles are followed by useful insights I have identified that might help overcome those obstacles. This is summarised in Figure X, which shows an HDR-specific barriers cascade: a route of overcoming obstacles through which individuals might be empowered and by which organisations might become more HDR-friendly.

Figure X: Obstacles and Resulting Insights in the HDR Opportunity Landscape

The obstacles and insights on the diagram are explained in the following subsections (the last of which covers some of the more pervasive obstacles that apply to all of the previous four HDR objectives).

7.3.1 An Objective for Better HDR: Data Awareness & Understanding

MAIN POINT: Data and its nature as remote, invisible and unrelatable. LIT LINKS: trapped. Abiteboul, legibility. HDI, effective access Gurstein. THESIS LINKS: not knowing. ‘in the dark’. both C4 & C5. visible C6. INSIGHT: Life information makes data relatable BBC LINK Life Concept Mapping (BBC) - life concept modelling diagrams OUTWORLD LINKS Facebook. ‘what presents the least technical view of data?’ as well as media things like netflix and spotify. SUBPOINT: THE DIASPORA - the diaspora. (but here we are interested in its effect, not the ‘where is it’). Also the fact that data gets separated from its context. (no need to mention silos here) ENDING It is a problem of representation

7.3.2 An Objective for Better HDR: Data Useability15

MAIN POINT: Data as immobile, inaccessible and not interrogable INSIGHT: DATA AS VERSATILE MATERIAL LIT REF PIM & SI REFs about doing stuff with data. LIT REF data enabled design https://uxdx.com/blog/data-enabled-design/ connected baby bottle . data enabled design canvas. INSIGHT: UNITING AND UNIFYING DATA LIT REF SPLINTERNET Uniting the diaspora LIT REF integration SI. OUTWORLD REF PDLockers SUBPOINT: Data by Reference Ref: programming. BBCREF watch history diagram SUBPOINT There is a negotiability element here: only those who encoded the data can fully explain it. links to provenance. ENDING: Need to change the nature of data. Just as files and databases have driven design thinking for the last half century, it is time to move up to the next level of DIKW. We need an information operating system.

7.3.3 An Objective for Better HDR: Ecosystem Awareness & Understanding

MAIN POINT: The ecosystem is complex and largely invisible. both the routine (e.g. accounts, auth, sync etc) and the hidden (data brokers, inferences, profiling etc HESTIA REF) INSIGHT: ECOSYSTEM INFORMATION OUTWORLD REF example: subscrab and inbox scanning. The opportunity of ecosystem detection SUBPOINT No one person can see the whole picture. (THESIS REF ref C4). SUBPOINT A lack of metadata BBC LINK. METADATA DIAGRAM FROM BBC ENDING = open up this new space that no-one is building for. for people to manage their digital world, they need a map. a vital first step on the road to giving individuals the ability to have oversight of their personal data ecosystem and take action within it. ENDING 2 = INSIGHT: PROVENANCE as a necessary step to understand both data and its context mike martin stuff LIT REF and C Jensen 2010 LIT REF tie into data non neutral stuff

7.3.4 An Objective for Better HDR: Ecosystem Negotiability

MAIN POINT: The relationship between orgs and individuals is shifting, they are amassing more power from data while also purposefully reducing individual agency. ILLUSTRATION: THE PANOPTICON LIT REF: JASPERSON POWER INSIGHT: HESTIA REF - FOUR LEVERS OF POWER REAL WORLD REFS: facebook api/feed closing, facebook change over location data, tiktok changing to legitimate interest, That guy who got banned from Facebook for letting people read their Facebook feed in a different way] [AND the blocking of accessibility readers] [and Chrome getting reinvented] [List of bullets] SILVER REF whispers of ‘moving away from consent to informing’ in pub sec SUBPOINT Companies acting in closed, introspective, non cooperative ways. Proprietary, incompatible silos Apple ecosystem. Google+ example. closed practices seen in c4. SUBPOINT THE INACCESSIBLE DATA SELF LIT REF cornford (and others?) ENDING: The focus on personal ecosystems, especially across providers, is almost non-existent. Business model or not, there is clearly a societal need for this. More than this, people need to be reconnected with their data selves rather than being pushed apart from it.

7.3.5 An Objective for Better HDR: Effective, Commercially Viable and Desirable Systems

MAIN POINT: HDR faces a particular challenge in that it requires a disruptive change, which no-one is screaming for and businesses are not currently motivated to invest in. It is hard to build effective systems in this space. We need Human Centred Information Systems faster horses example / ice disruptions? INSIGHT: NEW LIFE CAPABILITIES, PAIN RELIEVERS ref value proposition canvas. BACK SELF REF: VACATION EXAMPLE SUBPOINT: LACK OF INTEROPERABILITY INSIGHT: WE NEED TO TEACH COMPUTERS TO UNDERSTAND INFORMATION Semantic analysis & information standards.

BACKREF SELF ANNOTATING DATA DIAGRAM LIT REF Semantic Web OUTWORLD REF: CONTENT ANALYTICS / ECM standards example : shapes library, solid. ENDING 1 - need to show business value LIT REF VRM, [add a VRM screenshot] reduced liability increased accuracy WORLDREF the drive behind the data sharing ecosystem is better knowing users. self declared interests/data self would help ENDING 2: need to show value to individuals, give a new capability or solve a problem LIT REF environment/Abowd? LIT REF Value-centred design BBC REF HUMAN VALUES BBC

7.4 The landscape of opportunity: Four approaches to improving Human Data Relations

7.4.1 An Approach to Improving HDR: Discovery-Driven Activism

MAIN POINT: To actively use the legal rights, tools and capabilities available to discover what data is collected, how it is interpreted and used, how the ecosystem functions. To work together as collectives and make COMPARISONS. OUTREF analogy of theyworkforyou OUTREF Dehaye with Facebook OUTREF my work with Spotify, Netflix SUBPOINT: The power of collectives LITREF / OUTWORLD REFs Mahieu LITREF Digipower OUTREF Feed comparison Facebook political. (mention the unionisation angle OUTREF Uber) SUBPOINT Bootstrap the Data Understanding Industry OUTREF Ethi OUTREF Hestia SUBPOINT: AUDITING DATA HOLDERS (the triangulation of law, privacy policy and examining what they do) FRAME AS DIAGRAM individuals informing/powering collectives Collectives helping individuals Using Data to Demand Change in Practice => which in turn enables individuals with stronger capabilities and better transparency & insights ENDING: there is a role for independent actors and organisation to carry out activism - complaints, legal challenges, public relations, OUTWORLD REF noyb.eu, open rights group, labour/The Citizens

7.4.2 An Approach to Improving HDR: Building the Human-Centric Future

MAIN POINT: Design Ideas for a Human Centric Information System, illustrated with diagrams BBCREF A central home for your personal data BBCREF modelling data as life information BBCREF Happenings Diagram Time as unifier (LITREF TIME C2). What data IS to people (ref lenses) BBCREF (backref life concepts, then: Simplified model of presenting information to users) BBCREF Dashboard example SUBPOINT Capabilities BBCREF diagram What can users do (properties) Asking questions (THESISREF C5) BBCREF taxonomy diagram BBCREF Browsing by areas of life.. leads to: SUBPOINT Mental Models > Life- level systems, life partitioning BBCREF cluedo rooms LITREF Lenses etc C2 SUBPOINT Approaches by automatically finding entities ref back to semantics etc. (two arrows diagram back ref’d, and the Insight about semantic understanding) (can callback the subscrab example from above here too) Extraction and Learning systems BBC REF flows for entity identification BACKREF digital agents. like an assistant. [POSSIBLY CUT?] SUBPOINT Digital Self Curation & Inclusive Data Flows Litref VRM OUTREF BBC Wired article the potential of inclusive flows (build on provenance, rivers of data, LITREF streams) FRAME AS DIAGRAM Building new designs (reaching into understanding, LITREF data enabled design and Human values) Delivering new structural capabilities. Enabling new individual and collective perspectives. ENDING: Individuals Empowered with new Life / Ecosystem Information Capabilities.

7.4.3 An Approach to Improving HDR: Defending Autonomy and Nurturing the Information Landscape

MAIN POINT: That it is not just about Positive Change, there must also be Defensive Action, in the face of the active erosion of user autonomy (backref above diminishing agency). That this is an avenue of activist and grassroots work in its own right. some kinda visual? LITREF guard rails for the status quo INSIGHT: THE IMPORTANCE OF SEAMS Black Box diagram LITREF Storni magical design DERC REF Seams, JustEat etc. Facebook example. DERCREF the opportunity of scrapers & webaug LITREF right to repair SUBPOINT Surface Information Injustices. REALWORLD REF Frances Augen, Snowden, Assange.whistleblowers. but also can do this within interfaces. Build the features that should be there with a big “we can’t do this because X won’t let us” SUBPOINT promoting and developing standards, and better regulations OUTREF guidelines [GDPR guidelines I fed back on] OUTREF new European laws, DSA etc, to regulate the landscape ref back to end of C5, for policymakers FRAME AS DIAGRAM taking external protective action as collectives, surfacing, challenging, pushing for better enforcement of existing regulation ENDING: Seizing and holding the powers we are given and never giving them up. The price of freedom is eternal vigilance OUTWORLD ref cars OUTWORLD REF Apple OUTREF Ad blockers > Brave > facebook containers.

7.4.4 An Approach to Improving HDR: Winning Hearts and Minds: Teaching, Championing and Selling the Vision

MAIN POINT: That the nature of pursuing Human Data Relations causes for a radical reconfiguration of today’s data world. We need new systems (which means not only there need to be business drivers for those systems but also that existing organisations much choose or be compelled to invest in them), and people need to understand, use and see value in those systems. Therefore, there needs to be specific investment: SUBPOINT in Education, and Data Literacy SUBPOINT in Systems Building (just ee above) SUBPOINT in standards, information uniting the diaspora SUBPOINT in Researching New Business Models and Demonstrating Value of transparency and human centricity SUBPOINT in supporting Data Understanding Industry. empowering individuals as investigators. Tools to map their own ecosystems and unite their own personal data diaspora. FRAME AS DIAGRAM Structural work in upper right - standards Selling work in top level - show value to individuals Selling work in top level - show value to organisations Structural work in bottom right - systems Individual work in top left - empower and educate individuals all leading to new action of individuals in top right ENDING: that this is not just a technical problem, and not just a case of building new things. It’s about beginning and catalysing a cycle of constant feedback, of data enabled design and action research / iterative software and business model development - finding what works, championing it, selling it.

7.5 Thesis Conclusion

Bibliography

Bowyer, A. (2021) ‘Human-Data Interaction has two purposes: Personal Data Control and Life Information Exploration’. Available at: https://eprints.ncl.ac.uk/273832#.
Brest, P. (2010) ‘The Power of Theories of Change’, Stanford Social Innovation Review, 8(2), pp. 47–51.
Cavoukian, A. (2010) ‘Privacy by design: the definitive workshop. A foreword by Ann Cavoukian, Ph.D’, Identity in the Information Society, 3(2), pp. 247–251. doi: 10.1007/s12394-010-0062-y.
Crivellaro, C. et al. (2019) ‘Not-equal: Democratizing research in digital innovation for social justice’, Interactions, 26(2), pp. 70–73. doi: 10.1145/3301655.
Es, M. van, Guijt, I. and Vogel, I. (2015) ‘Hivos ToC Guidelines: Theory of Change Thinking in Practice’. The Hague, The Netherlands: Hivos.
‘Explainable AI: Making machines understandable for humans’ (no date). Available at: https://explainableai.com/ (Accessed: 16 June 2022).
Gillespie, T. and Seaver, N. (2016) ‘Critical Algorithm Studies - A Reading List’. Available at: https://socialmediacollective.org/reading-lists/critical-algorithm-studies/.
Harris, T. (2013) ‘A Call to Minimize Distraction Respect Users’ Attention’. Available at: http://www.minimizedistraction.com/.
Hoffman, W. (2014) Rethinking Personal Data : A New Lens for Strengthening Trust. May. World Economic Forum, p. 35. Available at: http://www3.weforum.org/docs/WEF_RethinkingPersonalData_ANewLens_Report_2014.pdf.
Jones, W. et al. (2006) ‘"It’s about the information stupid!": Why we need a separate field of human-information interaction’, Conference on Human Factors in Computing Systems - Proceedings, pp. 65–68. doi: 10.1145/1125451.1125469.
Karger, D. R. et al. (2005) ‘Haystack: A customizable general-purpose information management tool for end users of semistructured data’, in 2nd biennial conference on innovative data systems research, CIDR 2005, pp. 13–27. Available at: https://s3.amazonaws.com/academia.edu.documents/46870765/haystack.pdf.
Kelty, C. M. (2008) Geeks and Recursive Publics. Duke University Press, pp. 27–63.
Kirven, A. (2018) ‘Whose gig is it anyway: Technological change, workplace control and supervision, and workers’ rights in the gig economy’, U. Colo. L. Rev. HeinOnline, 89, p. 249.
Le Dantec, C. A. (2016) Designing publics. MIT Press.
Marchionini, G. (2008) ‘Human-information interaction research and development’, Library and Information Science Research, 30(3), pp. 165–174. doi: 10.1016/j.lisr.2008.07.001.
Mortier, R. et al. (2013) ‘Challenges & opportunities in human-data interaction’, University of Cambridge, Computer Laboratory. Citeseer. doi: 10.5210/fm.v17i5.4013.
Mortier, R. et al. (2014) ‘Human-data interaction: The human face of the data-driven society’, Available at SSRN 2508051. doi: 10.2139/ssrn.2508051.
MyData (2017) ‘Declaration - MyData.org’. Available at: https://mydata.org/declaration/ (Accessed: 8 November 2019).
‘Open rights group: Who we are’ (no date). Available at: https://www.openrightsgroup.org/who-we-are/ (Accessed: 16 June 2022).
‘Recursive Public (Discussion Page)’ (no date). Available at: https://wiki.p2pfoundation.net/Recursive_Public (Accessed: 16 June 2022).
Taplin, D. H. and Clark, H. (2012) ‘Theory of change basics: A primer on theory of change’, ActKnowledge, p. 9. Available at: http://www.theoryofchange.org/wp-content/uploads/toco_library/pdf/ToCBasics.pdf.
Taylor, L. (2017) ‘What is data justice? The case for connecting digital rights and freedoms globally’, Big Data and Society, 4(2). doi: 10.1177/2053951717736335.
Vlachokyriakos, V. et al. (2016) ‘Digital civics: Citizen empowerment with and through technology’, Conference on Human Factors in Computing Systems - Proceedings, 07-12-May-, pp. 1096–1099. doi: 10.1145/2851581.2886436.

  1. Diagram used here unchanged from Hivos ToC Guidelines (Es, Guijt and Vogel, 2015, p. p90) under a CC-BY-NC-SA 3.0 license, whose authors state that this diagram was adapted from earlier work by Wilber (1996), Keystone (2008) and Retolaza (2010, 2012).↩︎

  2. The group of HCI researchers involved in this panel were (with the exception of Raya Fidel) seemingly unaware of the existing HII field in library sciences as they positioned the publication as a call for a ‘new field’.↩︎

  3. Of course, there is some overlap; the reason that organisations hold data is so that they can interpret it (usually algorithmically) to inform decision-making. In this way, organisations could be seen to be doing LIU of service users’ lives for their own benefit. From a human-centric perspective, this grey area is situated as part of PDEC, as from the individual perspective, how organisations understand you through information will inform decisions that affect your life. Thus, this can be considered part of the reason why one might want to exert control over use of your data, rather than being part of exploiting data to gain self-insights and personal benefits.↩︎

  4. The illustrated processes assume reliance on existing data access processes such as GDPR, where the only access is through provision of a copy of one’s data. This is in fact, not ideal, as it creates divergent versions and will quickly become out-of-sync, however for the sake of simplicity this inefficiency is ignored here. Improvements upon this approach are explored in [INSERT REF]↩︎